Who Audits Whom, on What Substrate, with What Evidence? An Independence-Graded Audit Protocol for Agentic AI
Proposed audit protocol replaces binary independence with a graded scale for agentic AI—directly relevant as the EU AI Act demands proportionate assurance for high-risk systems.
Summary written by editorial AI · Source link below
arXiv:2609.18272v1 Announce Type: cross Abstract: Agentic AI systems plan, invoke tools and act with limited supervision; they are now both the subject of audits and, increasingly, the auditor. Independence, the foundation of assurance,is still applied to them as a binary. We argue that it must be graded along three orthogonal axes: principal independence (who controls the auditor), substrate independence (an auditor sharing the auditee's foundation-model family, toolchain or guardrails fails w
Editorial Analysis
Agentic AI can be both audit subject and auditor; a graded independence model helps enterprises demonstrate proportionate assurance under EU AI Act obligations.
Review your AI audit framework for scenarios where agentic systems audit other AI, and adopt graded independence criteria to satisfy upcoming EU AI Act requirements.
As AI systems increasingly audit each other, a graded independence model helps boards ensure audit credibility under the EU AI Act.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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